The ability to accurately predict the occurrence and location of various types of atmospheric phenomena has improved significantly over recent years due in part to advances in sensor technologies and remote sensing. Despite these advances, the insufficient spatial resolution of the meteorological analyses used in operational weather detection, and prediction often limit the ability to adequately resolve or identify important atmospheric features such as weather fronts, wind shifts, moisture and temperature changes, wind shear hazards and severe weather. Moreover, predictive applications such as atmospheric dispersion models are limited according to the accuracy of the meteorological analyses input data.
Many conventional atmospheric feature detection techniques are based on detecting gradients of meteorological parameters in a static, or Eulerian, analysis. These techniques have location accuracy limited by the spatial resolution of the Eulerian analysis. For example, phenomena associated with small-scale (e.g., less than 20 km) convective weather events are difficult to detect as it is not generally feasible to deploy sensors to achieve a sufficient sampling density for such phenomena. Atmospheric features extending over larger scales can also be difficult to detect due to localized features arising from geographic effects. In addition, sporadic features present in an observation can make it difficult to detect or identify features at any spatial scale.